Overview

Dataset statistics

Number of variables26
Number of observations20631
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 MiB
Average record size in memory208.0 B

Variable types

Numeric18
Categorical8

Alerts

setting_3 has constant value "100.0" Constant
Tag1 has constant value "518.67" Constant
Tag5 has constant value "14.62" Constant
Tag10 has constant value "1.3" Constant
Tag16 has constant value "0.03" Constant
Tag18 has constant value "2388" Constant
Tag19 has constant value "100.0" Constant
runtime is highly correlated with Tag2 and 9 other fieldsHigh correlation
Tag2 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag3 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag4 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag7 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag8 is highly correlated with Tag2 and 10 other fieldsHigh correlation
Tag9 is highly correlated with Tag14High correlation
Tag11 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag12 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag13 is highly correlated with Tag2 and 10 other fieldsHigh correlation
Tag14 is highly correlated with Tag9High correlation
Tag15 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag17 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag20 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag21 is highly correlated with runtime and 11 other fieldsHigh correlation
runtime is highly correlated with Tag2 and 9 other fieldsHigh correlation
Tag2 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag3 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag4 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag7 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag8 is highly correlated with Tag2 and 10 other fieldsHigh correlation
Tag9 is highly correlated with Tag14High correlation
Tag11 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag12 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag13 is highly correlated with Tag2 and 10 other fieldsHigh correlation
Tag14 is highly correlated with Tag9High correlation
Tag15 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag17 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag20 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag21 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag2 is highly correlated with Tag4 and 2 other fieldsHigh correlation
Tag4 is highly correlated with Tag2 and 9 other fieldsHigh correlation
Tag7 is highly correlated with Tag4 and 8 other fieldsHigh correlation
Tag8 is highly correlated with Tag4 and 5 other fieldsHigh correlation
Tag9 is highly correlated with Tag14High correlation
Tag11 is highly correlated with Tag2 and 9 other fieldsHigh correlation
Tag12 is highly correlated with Tag2 and 9 other fieldsHigh correlation
Tag13 is highly correlated with Tag4 and 5 other fieldsHigh correlation
Tag14 is highly correlated with Tag9High correlation
Tag15 is highly correlated with Tag4 and 5 other fieldsHigh correlation
Tag17 is highly correlated with Tag4 and 3 other fieldsHigh correlation
Tag20 is highly correlated with Tag4 and 3 other fieldsHigh correlation
Tag21 is highly correlated with Tag4 and 3 other fieldsHigh correlation
Tag19 is highly correlated with Tag16 and 6 other fieldsHigh correlation
Tag16 is highly correlated with Tag19 and 6 other fieldsHigh correlation
Tag1 is highly correlated with Tag19 and 6 other fieldsHigh correlation
Tag6 is highly correlated with Tag19 and 6 other fieldsHigh correlation
Tag5 is highly correlated with Tag19 and 6 other fieldsHigh correlation
setting_3 is highly correlated with Tag19 and 6 other fieldsHigh correlation
Tag10 is highly correlated with Tag19 and 6 other fieldsHigh correlation
Tag18 is highly correlated with Tag19 and 6 other fieldsHigh correlation
runtime is highly correlated with Tag2 and 11 other fieldsHigh correlation
Tag2 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag3 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag4 is highly correlated with runtime and 12 other fieldsHigh correlation
Tag7 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag8 is highly correlated with Tag2 and 12 other fieldsHigh correlation
Tag9 is highly correlated with runtime and 4 other fieldsHigh correlation
Tag11 is highly correlated with runtime and 12 other fieldsHigh correlation
Tag12 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag13 is highly correlated with Tag2 and 12 other fieldsHigh correlation
Tag14 is highly correlated with runtime and 6 other fieldsHigh correlation
Tag15 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag17 is highly correlated with runtime and 13 other fieldsHigh correlation
Tag20 is highly correlated with runtime and 11 other fieldsHigh correlation
Tag21 is highly correlated with runtime and 11 other fieldsHigh correlation
setting_1 has 413 (2.0%) zeros Zeros
setting_2 has 2070 (10.0%) zeros Zeros

Reproduction

Analysis started2022-03-16 19:51:09.605000
Analysis finished2022-03-16 19:51:41.050169
Duration31.45 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Asset id
Real number (ℝ≥0)

Distinct100
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.50656779
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:41.116699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q126
median52
Q377
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.22763291
Coefficient of variation (CV)0.5674544852
Kurtosis-1.219824128
Mean51.50656779
Median Absolute Deviation (MAD)26
Skewness-0.06781523411
Sum1062632
Variance854.2545255
MonotonicityIncreasing
2022-03-16T16:51:41.232853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69362
 
1.8%
92341
 
1.7%
96336
 
1.6%
67313
 
1.5%
83293
 
1.4%
2287
 
1.4%
95283
 
1.4%
64283
 
1.4%
86278
 
1.3%
17276
 
1.3%
Other values (90)17579
85.2%
ValueCountFrequency (%)
1192
0.9%
2287
1.4%
3179
0.9%
4189
0.9%
5269
1.3%
6188
0.9%
7259
1.3%
8150
0.7%
9201
1.0%
10222
1.1%
ValueCountFrequency (%)
100200
1.0%
99185
0.9%
98156
0.8%
97202
1.0%
96336
1.6%
95283
1.4%
94258
1.3%
93155
0.8%
92341
1.7%
91135
 
0.7%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.807862
Minimum1
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:41.333109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q152
median104
Q3156
95-th percentile230
Maximum362
Range361
Interquartile range (IQR)104

Descriptive statistics

Standard deviation68.88099018
Coefficient of variation (CV)0.6330515915
Kurtosis-0.2185391031
Mean108.807862
Median Absolute Deviation (MAD)52
Skewness0.4999039653
Sum2244815
Variance4744.590808
MonotonicityNot monotonic
2022-03-16T16:51:41.426321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100
 
0.5%
66100
 
0.5%
97100
 
0.5%
96100
 
0.5%
95100
 
0.5%
94100
 
0.5%
93100
 
0.5%
91100
 
0.5%
90100
 
0.5%
89100
 
0.5%
Other values (352)19631
95.2%
ValueCountFrequency (%)
1100
0.5%
2100
0.5%
3100
0.5%
4100
0.5%
5100
0.5%
6100
0.5%
7100
0.5%
8100
0.5%
9100
0.5%
10100
0.5%
ValueCountFrequency (%)
3621
< 0.1%
3611
< 0.1%
3601
< 0.1%
3591
< 0.1%
3581
< 0.1%
3571
< 0.1%
3561
< 0.1%
3551
< 0.1%
3541
< 0.1%
3531
< 0.1%

setting_1
Real number (ℝ)

ZEROS

Distinct158
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.870146866 × 10-6
Minimum-0.0087
Maximum0.0087
Zeros413
Zeros (%)2.0%
Negative10061
Negative (%)48.8%
Memory size161.3 KiB
2022-03-16T16:51:41.536122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0087
5-th percentile-0.0037
Q1-0.0015
median0
Q30.0015
95-th percentile0.0036
Maximum0.0087
Range0.0174
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.002187313449
Coefficient of variation (CV)-246.5926982
Kurtosis-0.009131624273
Mean-8.870146866 × 10-6
Median Absolute Deviation (MAD)0.0015
Skewness-0.02476626674
Sum-0.183
Variance4.784340124 × 10-6
MonotonicityNot monotonic
2022-03-16T16:51:41.756421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0413
 
2.0%
0.0002398
 
1.9%
0.0004394
 
1.9%
-0.0005390
 
1.9%
0.0001382
 
1.9%
0.0005381
 
1.8%
0.0006379
 
1.8%
-0.0006375
 
1.8%
0.0003364
 
1.8%
0.0009362
 
1.8%
Other values (148)16793
81.4%
ValueCountFrequency (%)
-0.00871
 
< 0.1%
-0.00861
 
< 0.1%
-0.00841
 
< 0.1%
-0.00812
< 0.1%
-0.00781
 
< 0.1%
-0.00751
 
< 0.1%
-0.00743
< 0.1%
-0.00731
 
< 0.1%
-0.00722
< 0.1%
-0.0072
< 0.1%
ValueCountFrequency (%)
0.00871
 
< 0.1%
0.00831
 
< 0.1%
0.00771
 
< 0.1%
0.00761
 
< 0.1%
0.00743
< 0.1%
0.00731
 
< 0.1%
0.00724
< 0.1%
0.00712
< 0.1%
0.0072
< 0.1%
0.00692
< 0.1%

setting_2
Real number (ℝ)

ZEROS

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.350831273 × 10-6
Minimum-0.0006
Maximum0.0006
Zeros2070
Zeros (%)10.0%
Negative9225
Negative (%)44.7%
Memory size161.3 KiB
2022-03-16T16:51:41.829853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0006
5-th percentile-0.0004
Q1-0.0002
median0
Q30.0003
95-th percentile0.0005
Maximum0.0006
Range0.0012
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.0002930621246
Coefficient of variation (CV)124.6631895
Kurtosis-1.130447016
Mean2.350831273 × 10-6
Median Absolute Deviation (MAD)0.0003
Skewness0.009085119712
Sum0.0485
Variance8.588540886 × 10-8
MonotonicityNot monotonic
2022-03-16T16:51:41.893649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
-0.00032104
10.2%
0.00012097
10.2%
02070
10.0%
0.00032065
10.0%
-0.00042051
9.9%
-0.00022049
9.9%
0.00022038
9.9%
-0.00012029
9.8%
0.00041997
9.7%
0.00051068
5.2%
Other values (3)1063
5.2%
ValueCountFrequency (%)
-0.000634
 
0.2%
-0.0005958
4.6%
-0.00042051
9.9%
-0.00032104
10.2%
-0.00022049
9.9%
-0.00012029
9.8%
02070
10.0%
0.00012097
10.2%
0.00022038
9.9%
0.00032065
10.0%
ValueCountFrequency (%)
0.000671
 
0.3%
0.00051068
5.2%
0.00041997
9.7%
0.00032065
10.0%
0.00022038
9.9%
0.00012097
10.2%
02070
10.0%
-0.00012029
9.8%
-0.00022049
9.9%
-0.00032104
10.2%

setting_3
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
100.0
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.020631
100.0%

Length

2022-03-16T16:51:41.962425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-16T16:51:42.008845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
100.020631
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tag1
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
518.67
20631 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row518.67
2nd row518.67
3rd row518.67
4th row518.67
5th row518.67

Common Values

ValueCountFrequency (%)
518.6720631
100.0%

Length

2022-03-16T16:51:42.046754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-16T16:51:42.092473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
518.6720631
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tag2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct310
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.6809335
Minimum641.21
Maximum644.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:42.145763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum641.21
5-th percentile641.92
Q1642.325
median642.64
Q3643
95-th percentile643.58
Maximum644.53
Range3.32
Interquartile range (IQR)0.675

Descriptive statistics

Standard deviation0.5000532701
Coefficient of variation (CV)0.000778073915
Kurtosis-0.1120429443
Mean642.6809335
Median Absolute Deviation (MAD)0.34
Skewness0.3165258909
Sum13259150.34
Variance0.2500532729
MonotonicityNot monotonic
2022-03-16T16:51:42.226404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642.5190
 
0.9%
642.56189
 
0.9%
642.53188
 
0.9%
642.6184
 
0.9%
642.67179
 
0.9%
642.44175
 
0.8%
642.63175
 
0.8%
642.57172
 
0.8%
642.64168
 
0.8%
642.73167
 
0.8%
Other values (300)18844
91.3%
ValueCountFrequency (%)
641.211
 
< 0.1%
641.252
< 0.1%
641.273
< 0.1%
641.34
< 0.1%
641.311
 
< 0.1%
641.322
< 0.1%
641.332
< 0.1%
641.341
 
< 0.1%
641.351
 
< 0.1%
641.362
< 0.1%
ValueCountFrequency (%)
644.532
< 0.1%
644.51
< 0.1%
644.471
< 0.1%
644.441
< 0.1%
644.391
< 0.1%
644.371
< 0.1%
644.351
< 0.1%
644.341
< 0.1%
644.311
< 0.1%
644.32
< 0.1%

Tag3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3012
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1590.523119
Minimum1571.04
Maximum1616.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:42.312261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1571.04
5-th percentile1581.11
Q11586.26
median1590.1
Q31594.38
95-th percentile1601.47
Maximum1616.91
Range45.87
Interquartile range (IQR)8.12

Descriptive statistics

Standard deviation6.13114952
Coefficient of variation (CV)0.00385480063
Kurtosis0.007761822403
Mean1590.523119
Median Absolute Deviation (MAD)4.05
Skewness0.3089458061
Sum32814082.46
Variance37.59099443
MonotonicityNot monotonic
2022-03-16T16:51:42.410769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1590.127
 
0.1%
1589.7626
 
0.1%
1589.9825
 
0.1%
1592.1125
 
0.1%
1587.8624
 
0.1%
1584.9523
 
0.1%
1590.5423
 
0.1%
1589.0823
 
0.1%
1589.4423
 
0.1%
1587.8222
 
0.1%
Other values (3002)20390
98.8%
ValueCountFrequency (%)
1571.041
< 0.1%
1571.061
< 0.1%
1571.841
< 0.1%
1571.991
< 0.1%
1572.341
< 0.1%
1572.41
< 0.1%
1572.461
< 0.1%
1572.671
< 0.1%
1572.761
< 0.1%
1572.981
< 0.1%
ValueCountFrequency (%)
1616.911
< 0.1%
1614.931
< 0.1%
1614.721
< 0.1%
1613.621
< 0.1%
1613.291
< 0.1%
1612.881
< 0.1%
1612.631
< 0.1%
1612.111
< 0.1%
1611.921
< 0.1%
1611.571
< 0.1%

Tag4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4051
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1408.933782
Minimum1382.25
Maximum1441.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:42.497535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1382.25
5-th percentile1395.62
Q11402.36
median1408.04
Q31414.555
95-th percentile1425.67
Maximum1441.49
Range59.24
Interquartile range (IQR)12.195

Descriptive statistics

Standard deviation9.000604781
Coefficient of variation (CV)0.006388238324
Kurtosis-0.1636808632
Mean1408.933782
Median Absolute Deviation (MAD)6.04
Skewness0.4431943409
Sum29067712.85
Variance81.01088642
MonotonicityNot monotonic
2022-03-16T16:51:42.581442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1409.0120
 
0.1%
1404.4718
 
0.1%
1407.1518
 
0.1%
1407.0218
 
0.1%
1414.0318
 
0.1%
1410.5418
 
0.1%
1403.2317
 
0.1%
1407.1816
 
0.1%
1410.5716
 
0.1%
1401.2716
 
0.1%
Other values (4041)20456
99.2%
ValueCountFrequency (%)
1382.251
< 0.1%
1385.191
< 0.1%
1385.751
< 0.1%
1386.291
< 0.1%
1386.431
< 0.1%
1386.691
< 0.1%
1387.161
< 0.1%
1387.361
< 0.1%
1387.381
< 0.1%
1387.51
< 0.1%
ValueCountFrequency (%)
1441.491
< 0.1%
1438.961
< 0.1%
1438.511
< 0.1%
1438.411
< 0.1%
1438.221
< 0.1%
1438.161
< 0.1%
1438.11
< 0.1%
1437.981
< 0.1%
1437.881
< 0.1%
1437.811
< 0.1%

Tag5
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
14.62
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14.62
2nd row14.62
3rd row14.62
4th row14.62
5th row14.62

Common Values

ValueCountFrequency (%)
14.6220631
100.0%

Length

2022-03-16T16:51:42.657270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-16T16:51:42.700791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
14.6220631
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tag6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
21.61
20225 
21.6
 
406

Length

Max length5
Median length5
Mean length4.980320876
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21.61
2nd row21.61
3rd row21.61
4th row21.61
5th row21.61

Common Values

ValueCountFrequency (%)
21.6120225
98.0%
21.6406
 
2.0%

Length

2022-03-16T16:51:42.740323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-16T16:51:42.791125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
21.6120225
98.0%
21.6406
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tag7
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct513
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.3677112
Minimum549.85
Maximum556.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:42.861758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum549.85
5-th percentile551.74
Q1552.81
median553.44
Q3554.01
95-th percentile554.69
Maximum556.06
Range6.21
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.8850922577
Coefficient of variation (CV)0.001599464948
Kurtosis-0.1579492184
Mean553.3677112
Median Absolute Deviation (MAD)0.6
Skewness-0.394328939
Sum11416529.25
Variance0.7833883046
MonotonicityNot monotonic
2022-03-16T16:51:42.966423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
553.62116
 
0.6%
553.76115
 
0.6%
553.72110
 
0.5%
553.94110
 
0.5%
553.43108
 
0.5%
553.74107
 
0.5%
553.75106
 
0.5%
554105
 
0.5%
553.9104
 
0.5%
553.52103
 
0.5%
Other values (503)19547
94.7%
ValueCountFrequency (%)
549.851
< 0.1%
550.341
< 0.1%
550.351
< 0.1%
550.421
< 0.1%
550.431
< 0.1%
550.482
< 0.1%
550.491
< 0.1%
550.51
< 0.1%
550.512
< 0.1%
550.521
< 0.1%
ValueCountFrequency (%)
556.061
< 0.1%
555.861
< 0.1%
555.721
< 0.1%
555.71
< 0.1%
555.671
< 0.1%
555.661
< 0.1%
555.641
< 0.1%
555.611
< 0.1%
555.61
< 0.1%
555.581
< 0.1%

Tag8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.096652
Minimum2387.9
Maximum2388.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:43.061965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2387.9
5-th percentile2387.99
Q12388.05
median2388.09
Q32388.14
95-th percentile2388.22
Maximum2388.56
Range0.66
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.07098547889
Coefficient of variation (CV)2.972470936 × 10-5
Kurtosis0.3331490099
Mean2388.096652
Median Absolute Deviation (MAD)0.05
Skewness0.4794108607
Sum49268822.02
Variance0.005038938213
MonotonicityNot monotonic
2022-03-16T16:51:43.158402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.111181
 
5.7%
2388.11159
 
5.6%
2388.091149
 
5.6%
2388.081126
 
5.5%
2388.071077
 
5.2%
2388.121069
 
5.2%
2388.061050
 
5.1%
2388.131033
 
5.0%
2388.051013
 
4.9%
2388.04910
 
4.4%
Other values (43)9864
47.8%
ValueCountFrequency (%)
2387.91
 
< 0.1%
2387.913
 
< 0.1%
2387.929
 
< 0.1%
2387.9316
 
0.1%
2387.9433
 
0.2%
2387.9572
 
0.3%
2387.96145
 
0.7%
2387.97201
1.0%
2387.98339
1.6%
2387.99426
2.1%
ValueCountFrequency (%)
2388.561
 
< 0.1%
2388.521
 
< 0.1%
2388.51
 
< 0.1%
2388.461
 
< 0.1%
2388.442
 
< 0.1%
2388.371
 
< 0.1%
2388.361
 
< 0.1%
2388.352
 
< 0.1%
2388.3413
0.1%
2388.3313
0.1%

Tag9
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6403
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9065.242941
Minimum9021.73
Maximum9244.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:43.260972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9021.73
5-th percentile9042.55
Q19053.1
median9060.66
Q39069.42
95-th percentile9109.98
Maximum9244.59
Range222.86
Interquartile range (IQR)16.32

Descriptive statistics

Standard deviation22.08287953
Coefficient of variation (CV)0.002435994233
Kurtosis9.378681311
Mean9065.242941
Median Absolute Deviation (MAD)8.13
Skewness2.555364867
Sum187025027.1
Variance487.6535681
MonotonicityNot monotonic
2022-03-16T16:51:43.354864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9058.8816
 
0.1%
9060.3715
 
0.1%
9060.5515
 
0.1%
9056.8615
 
0.1%
9063.2215
 
0.1%
9060.8715
 
0.1%
9054.5414
 
0.1%
9061.0514
 
0.1%
9057.9514
 
0.1%
9065.4714
 
0.1%
Other values (6393)20484
99.3%
ValueCountFrequency (%)
9021.731
< 0.1%
9023.851
< 0.1%
9024.271
< 0.1%
9024.421
< 0.1%
9025.221
< 0.1%
9025.291
< 0.1%
9026.081
< 0.1%
9026.171
< 0.1%
9026.191
< 0.1%
9026.661
< 0.1%
ValueCountFrequency (%)
9244.591
< 0.1%
9239.761
< 0.1%
9228.531
< 0.1%
9226.61
< 0.1%
9224.871
< 0.1%
9224.531
< 0.1%
9223.561
< 0.1%
9221.311
< 0.1%
9220.881
< 0.1%
9219.811
< 0.1%

Tag10
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
1.3
20631 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.3
2nd row1.3
3rd row1.3
4th row1.3
5th row1.3

Common Values

ValueCountFrequency (%)
1.320631
100.0%

Length

2022-03-16T16:51:43.561309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-16T16:51:43.605219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.320631
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tag11
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct159
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.54116815
Minimum46.85
Maximum48.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:43.655401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum46.85
5-th percentile47.15
Q147.35
median47.51
Q347.7
95-th percentile48.045
Maximum48.53
Range1.68
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.2670873986
Coefficient of variation (CV)0.005618023474
Kurtosis-0.1721918817
Mean47.54116815
Median Absolute Deviation (MAD)0.18
Skewness0.4693290936
Sum980821.84
Variance0.07133567851
MonotonicityNot monotonic
2022-03-16T16:51:43.770715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.46341
 
1.7%
47.57338
 
1.6%
47.49332
 
1.6%
47.45332
 
1.6%
47.47331
 
1.6%
47.52326
 
1.6%
47.37321
 
1.6%
47.48319
 
1.5%
47.44318
 
1.5%
47.43311
 
1.5%
Other values (149)17362
84.2%
ValueCountFrequency (%)
46.851
 
< 0.1%
46.863
< 0.1%
46.882
 
< 0.1%
46.891
 
< 0.1%
46.91
 
< 0.1%
46.911
 
< 0.1%
46.923
< 0.1%
46.933
< 0.1%
46.946
< 0.1%
46.956
< 0.1%
ValueCountFrequency (%)
48.531
 
< 0.1%
48.521
 
< 0.1%
48.481
 
< 0.1%
48.431
 
< 0.1%
48.414
< 0.1%
48.44
< 0.1%
48.393
< 0.1%
48.381
 
< 0.1%
48.372
 
< 0.1%
48.355
< 0.1%

Tag12
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct427
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.41347
Minimum518.69
Maximum523.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:43.893511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum518.69
5-th percentile520.04
Q1520.96
median521.48
Q3521.95
95-th percentile522.5
Maximum523.38
Range4.69
Interquartile range (IQR)0.99

Descriptive statistics

Standard deviation0.7375533922
Coefficient of variation (CV)0.001414526925
Kurtosis-0.1449165712
Mean521.41347
Median Absolute Deviation (MAD)0.5
Skewness-0.4424072433
Sum10757281.3
Variance0.5439850064
MonotonicityNot monotonic
2022-03-16T16:51:43.997773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.63143
 
0.7%
521.42136
 
0.7%
521.35131
 
0.6%
521.56129
 
0.6%
521.66126
 
0.6%
521.54125
 
0.6%
521.69124
 
0.6%
521.5123
 
0.6%
521.46121
 
0.6%
521.43121
 
0.6%
Other values (417)19352
93.8%
ValueCountFrequency (%)
518.691
< 0.1%
518.832
< 0.1%
518.941
< 0.1%
518.951
< 0.1%
518.981
< 0.1%
518.991
< 0.1%
519.011
< 0.1%
519.021
< 0.1%
519.031
< 0.1%
519.062
< 0.1%
ValueCountFrequency (%)
523.382
< 0.1%
523.351
< 0.1%
523.311
< 0.1%
523.271
< 0.1%
523.262
< 0.1%
523.251
< 0.1%
523.241
< 0.1%
523.231
< 0.1%
523.211
< 0.1%
523.21
< 0.1%

Tag13
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.096152
Minimum2387.88
Maximum2388.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:44.082190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2387.88
5-th percentile2387.99
Q12388.04
median2388.09
Q32388.14
95-th percentile2388.23
Maximum2388.56
Range0.68
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.0719189157
Coefficient of variation (CV)3.011558627 × 10-5
Kurtosis0.3872437577
Mean2388.096152
Median Absolute Deviation (MAD)0.05
Skewness0.469792422
Sum49268811.72
Variance0.005172330435
MonotonicityNot monotonic
2022-03-16T16:51:44.163740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.11164
 
5.6%
2388.091144
 
5.5%
2388.081129
 
5.5%
2388.111127
 
5.5%
2388.071112
 
5.4%
2388.121099
 
5.3%
2388.061005
 
4.9%
2388.05987
 
4.8%
2388.13976
 
4.7%
2388.04952
 
4.6%
Other values (46)9936
48.2%
ValueCountFrequency (%)
2387.881
 
< 0.1%
2387.891
 
< 0.1%
2387.91
 
< 0.1%
2387.912
 
< 0.1%
2387.9212
 
0.1%
2387.9319
 
0.1%
2387.9454
 
0.3%
2387.9595
0.5%
2387.96170
0.8%
2387.97219
1.1%
ValueCountFrequency (%)
2388.561
 
< 0.1%
2388.551
 
< 0.1%
2388.541
 
< 0.1%
2388.491
 
< 0.1%
2388.441
 
< 0.1%
2388.392
 
< 0.1%
2388.373
 
< 0.1%
2388.366
< 0.1%
2388.357
< 0.1%
2388.348
< 0.1%

Tag14
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6078
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8143.752722
Minimum8099.94
Maximum8293.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:44.263014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8099.94
5-th percentile8122.505
Q18133.245
median8140.54
Q38148.31
95-th percentile8181.405
Maximum8293.72
Range193.78
Interquartile range (IQR)15.065

Descriptive statistics

Standard deviation19.07617598
Coefficient of variation (CV)0.002342430649
Kurtosis8.85466446
Mean8143.752722
Median Absolute Deviation (MAD)7.54
Skewness2.372553647
Sum168013762.4
Variance363.9004899
MonotonicityNot monotonic
2022-03-16T16:51:44.368461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8138.8917
 
0.1%
8141.8517
 
0.1%
8136.8916
 
0.1%
8140.7915
 
0.1%
8140.6515
 
0.1%
8140.4915
 
0.1%
8140.3315
 
0.1%
8140.8915
 
0.1%
8136.6915
 
0.1%
8140.9715
 
0.1%
Other values (6068)20476
99.2%
ValueCountFrequency (%)
8099.941
< 0.1%
8101.491
< 0.1%
8102.821
< 0.1%
8103.271
< 0.1%
8103.771
< 0.1%
8103.981
< 0.1%
8104.461
< 0.1%
8104.781
< 0.1%
8104.821
< 0.1%
8105.221
< 0.1%
ValueCountFrequency (%)
8293.721
< 0.1%
8290.251
< 0.1%
8289.631
< 0.1%
8288.261
< 0.1%
8282.51
< 0.1%
8279.861
< 0.1%
8279.791
< 0.1%
8276.21
< 0.1%
8274.651
< 0.1%
8273.151
< 0.1%

Tag15
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1918
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.442145582
Minimum8.3249
Maximum8.5848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:44.478447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8.3249
5-th percentile8.3859
Q18.4149
median8.4389
Q38.4656
95-th percentile8.511
Maximum8.5848
Range0.2599
Interquartile range (IQR)0.0507

Descriptive statistics

Standard deviation0.03750503795
Coefficient of variation (CV)0.004442595498
Kurtosis-0.1214299983
Mean8.442145582
Median Absolute Deviation (MAD)0.0252
Skewness0.3882585825
Sum174169.9055
Variance0.001406627872
MonotonicityNot monotonic
2022-03-16T16:51:44.567746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.430938
 
0.2%
8.431837
 
0.2%
8.446836
 
0.2%
8.444235
 
0.2%
8.412834
 
0.2%
8.445332
 
0.2%
8.444632
 
0.2%
8.437131
 
0.2%
8.420931
 
0.2%
8.422631
 
0.2%
Other values (1908)20294
98.4%
ValueCountFrequency (%)
8.32491
< 0.1%
8.32791
< 0.1%
8.33031
< 0.1%
8.33582
< 0.1%
8.33651
< 0.1%
8.33871
< 0.1%
8.341
< 0.1%
8.34091
< 0.1%
8.34271
< 0.1%
8.34281
< 0.1%
ValueCountFrequency (%)
8.58481
< 0.1%
8.58361
< 0.1%
8.56781
< 0.1%
8.56711
< 0.1%
8.56681
< 0.1%
8.56651
< 0.1%
8.56541
< 0.1%
8.56481
< 0.1%
8.56461
< 0.1%
8.56411
< 0.1%

Tag16
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
0.03
20631 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.03
2nd row0.03
3rd row0.03
4th row0.03
5th row0.03

Common Values

ValueCountFrequency (%)
0.0320631
100.0%

Length

2022-03-16T16:51:44.647059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-16T16:51:44.688034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0320631
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tag17
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean393.2106539
Minimum388
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:44.720546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum388
5-th percentile391
Q1392
median393
Q3394
95-th percentile396
Maximum400
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.548763025
Coefficient of variation (CV)0.003938761601
Kurtosis-0.0391740435
Mean393.2106539
Median Absolute Deviation (MAD)1
Skewness0.3531256609
Sum8112329
Variance2.398666906
MonotonicityNot monotonic
2022-03-16T16:51:44.814473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3935445
26.4%
3924578
22.2%
3944063
19.7%
3952339
11.3%
3912022
 
9.8%
3961185
 
5.7%
390452
 
2.2%
397436
 
2.1%
39872
 
0.3%
38930
 
0.1%
Other values (3)9
 
< 0.1%
ValueCountFrequency (%)
3881
 
< 0.1%
38930
 
0.1%
390452
 
2.2%
3912022
 
9.8%
3924578
22.2%
3935445
26.4%
3944063
19.7%
3952339
11.3%
3961185
 
5.7%
397436
 
2.1%
ValueCountFrequency (%)
4001
 
< 0.1%
3997
 
< 0.1%
39872
 
0.3%
397436
 
2.1%
3961185
 
5.7%
3952339
11.3%
3944063
19.7%
3935445
26.4%
3924578
22.2%
3912022
 
9.8%

Tag18
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
2388
20631 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2388
2nd row2388
3rd row2388
4th row2388
5th row2388

Common Values

ValueCountFrequency (%)
238820631
100.0%

Length

2022-03-16T16:51:44.899724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-16T16:51:44.962194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
238820631
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tag19
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
100.0
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.020631
100.0%

Length

2022-03-16T16:51:45.010661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-16T16:51:45.057254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
100.020631
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tag20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct120
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.81627066
Minimum38.14
Maximum39.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:45.113755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum38.14
5-th percentile38.49
Q138.7
median38.83
Q338.95
95-th percentile39.09
Maximum39.43
Range1.29
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.1807464279
Coefficient of variation (CV)0.004656460417
Kurtosis-0.1128291058
Mean38.81627066
Median Absolute Deviation (MAD)0.12
Skewness-0.3584452022
Sum800818.48
Variance0.03266927119
MonotonicityNot monotonic
2022-03-16T16:51:45.214221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.86485
 
2.4%
38.89476
 
2.3%
38.82472
 
2.3%
38.87460
 
2.2%
38.85458
 
2.2%
38.83457
 
2.2%
38.84455
 
2.2%
38.88452
 
2.2%
38.81447
 
2.2%
38.8447
 
2.2%
Other values (110)16022
77.7%
ValueCountFrequency (%)
38.141
 
< 0.1%
38.161
 
< 0.1%
38.181
 
< 0.1%
38.191
 
< 0.1%
38.21
 
< 0.1%
38.211
 
< 0.1%
38.223
 
< 0.1%
38.235
< 0.1%
38.247
< 0.1%
38.259
< 0.1%
ValueCountFrequency (%)
39.431
 
< 0.1%
39.411
 
< 0.1%
39.341
 
< 0.1%
39.321
 
< 0.1%
39.312
 
< 0.1%
39.32
 
< 0.1%
39.293
 
< 0.1%
39.281
 
< 0.1%
39.2710
< 0.1%
39.267
< 0.1%

Tag21
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4745
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.28970536
Minimum22.8942
Maximum23.6184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2022-03-16T16:51:45.315521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum22.8942
5-th percentile23.09345
Q123.2218
median23.2979
Q323.3668
95-th percentile23.4535
Maximum23.6184
Range0.7242
Interquartile range (IQR)0.145

Descriptive statistics

Standard deviation0.1082508747
Coefficient of variation (CV)0.004648013921
Kurtosis-0.1170394481
Mean23.28970536
Median Absolute Deviation (MAD)0.0724
Skewness-0.3503749622
Sum480489.9113
Variance0.01171825188
MonotonicityNot monotonic
2022-03-16T16:51:45.546717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.322223
 
0.1%
23.302917
 
0.1%
23.289616
 
0.1%
23.372516
 
0.1%
23.37115
 
0.1%
23.349115
 
0.1%
23.349715
 
0.1%
23.331515
 
0.1%
23.300215
 
0.1%
23.330915
 
0.1%
Other values (4735)20469
99.2%
ValueCountFrequency (%)
22.89421
< 0.1%
22.90711
< 0.1%
22.91221
< 0.1%
22.93051
< 0.1%
22.93331
< 0.1%
22.93371
< 0.1%
22.93641
< 0.1%
22.93962
< 0.1%
22.93981
< 0.1%
22.94021
< 0.1%
ValueCountFrequency (%)
23.61841
< 0.1%
23.61271
< 0.1%
23.60641
< 0.1%
23.60051
< 0.1%
23.59831
< 0.1%
23.5891
< 0.1%
23.58622
< 0.1%
23.58581
< 0.1%
23.58251
< 0.1%
23.57911
< 0.1%

Interactions

2022-03-16T16:51:38.689457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:11.554718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:13.191852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:14.816810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:16.308633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:17.974896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:19.453643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:21.095280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:22.719507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:24.258768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:25.913922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:27.430700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:29.113189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:30.590319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:32.265972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:33.960185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:35.449898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:37.153336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:38.772196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:11.638820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:13.270709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:14.893575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:16.393465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:18.052768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:19.536502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:21.176711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:22.795839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:24.348563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:26.004697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:27.520839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:29.187948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:30.794240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:32.346595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:34.041905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:35.539542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:37.229699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:38.986990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:11.821248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:13.367825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:14.973323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:16.473843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:18.131149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:19.614777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:21.265648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:22.884118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:24.430847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:26.110224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:27.599869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:29.267791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:30.872417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:32.432218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:34.122338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:35.624500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:37.325596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:39.068204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:11.907889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:13.450940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:15.053489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:16.562295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:18.222361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:19.692116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:21.345442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:22.986876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:24.526433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:26.216251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:27.690029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:29.347479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:30.953492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:32.515455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:34.199096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:35.709262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:37.414626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:39.163650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:11.986862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:13.533903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:15.135981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:16.666168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:18.307977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:19.931082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:21.438193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:23.079830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:24.613599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:26.306135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:27.779809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:29.428345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:31.047477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:32.601379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:34.286720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:35.793250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:37.518380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:39.249037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:12.064773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:13.613615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:15.221292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:16.750668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:18.392519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:20.029958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:21.515748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:23.162774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:24.693928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:26.388374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:27.877444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:29.507585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:31.130827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:32.686609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:34.364321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:35.877405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:37.605541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:39.326017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:12.148803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:13.688745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:15.318964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:16.825227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:18.475234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:20.115148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:21.611532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:23.239096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:24.767775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-16T16:51:32.165586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:33.868167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:35.360498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:37.070951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T16:51:38.605206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-16T16:51:45.644994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-16T16:51:45.815208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-16T16:51:45.979914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-16T16:51:46.124233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-16T16:51:46.225950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-16T16:51:40.473399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-16T16:51:40.897177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Asset idruntimesetting_1setting_2setting_3Tag1Tag2Tag3Tag4Tag5Tag6Tag7Tag8Tag9Tag10Tag11Tag12Tag13Tag14Tag15Tag16Tag17Tag18Tag19Tag20Tag21
011-0.0007-0.0004100.0518.67641.821589.701400.6014.6221.61554.362388.069046.191.347.47521.662388.028138.628.41950.033922388100.039.0623.4190
1120.0019-0.0003100.0518.67642.151591.821403.1414.6221.61553.752388.049044.071.347.49522.282388.078131.498.43180.033922388100.039.0023.4236
213-0.00430.0003100.0518.67642.351587.991404.2014.6221.61554.262388.089052.941.347.27522.422388.038133.238.41780.033902388100.038.9523.3442
3140.00070.0000100.0518.67642.351582.791401.8714.6221.61554.452388.119049.481.347.13522.862388.088133.838.36820.033922388100.038.8823.3739
415-0.0019-0.0002100.0518.67642.371582.851406.2214.6221.61554.002388.069055.151.347.28522.192388.048133.808.42940.033932388100.038.9023.4044
516-0.0043-0.0001100.0518.67642.101584.471398.3714.6221.61554.672388.029049.681.347.16521.682388.038132.858.41080.033912388100.038.9823.3669
6170.00100.0001100.0518.67642.481592.321397.7714.6221.61554.342388.029059.131.347.36522.322388.038132.328.39740.033922388100.039.1023.3774
718-0.00340.0003100.0518.67642.561582.961400.9714.6221.61553.852388.009040.801.347.24522.472388.038131.078.40760.033912388100.038.9723.3106
8190.00080.0001100.0518.67642.121590.981394.8014.6221.61553.692388.059046.461.347.29521.792388.058125.698.37280.033922388100.039.0523.4066
9110-0.00330.0001100.0518.67641.711591.241400.4614.6221.61553.592388.059051.701.347.03521.792388.068129.388.42860.033932388100.038.9523.4694

Last rows

Asset idruntimesetting_1setting_2setting_3Tag1Tag2Tag3Tag4Tag5Tag6Tag7Tag8Tag9Tag10Tag11Tag12Tag13Tag14Tag15Tag16Tag17Tag18Tag19Tag20Tag21
20621100191-0.0005-0.0000100.0518.67643.691610.871427.1914.6221.61551.782388.269068.901.348.07519.802388.288143.568.50920.033982388100.038.3923.1218
20622100192-0.00090.0001100.0518.67643.531601.231419.4814.6221.61551.142388.179060.451.348.18520.592388.218143.468.48920.033972388100.038.5623.0770
20623100193-0.00010.0002100.0518.67643.091599.811428.9314.6221.61552.042388.299067.571.348.19520.112388.198142.028.54240.033972388100.038.4723.0230
20624100194-0.00110.0003100.0518.67643.721597.291427.4114.6221.61551.992388.239068.851.348.12519.552388.228139.678.52150.033942388100.038.3823.1324
20625100195-0.0002-0.0001100.0518.67643.411600.041431.9014.6221.61551.422388.239069.691.348.22519.712388.288142.908.55190.033942388100.038.1423.1923
20626100196-0.0004-0.0003100.0518.67643.491597.981428.6314.6221.61551.432388.199065.521.348.07519.492388.268137.608.49560.033972388100.038.4922.9735
20627100197-0.0016-0.0005100.0518.67643.541604.501433.5814.6221.61550.862388.239065.111.348.04519.682388.228136.508.51390.033952388100.038.3023.1594
206281001980.00040.0000100.0518.67643.421602.461428.1814.6221.61550.942388.249065.901.348.09520.012388.248141.058.56460.033982388100.038.4422.9333
20629100199-0.00110.0003100.0518.67643.231605.261426.5314.6221.61550.682388.259073.721.348.39519.672388.238139.298.53890.033952388100.038.2923.0640
20630100200-0.0032-0.0005100.0518.67643.851600.381432.1414.6221.61550.792388.269061.481.348.20519.302388.268137.338.50360.033962388100.038.3723.0522